The Role Of The Finance

FINANCE PROFESSIONAL -TO-CASH PROCESS

As technology continues to evolve, organisations are finding ways to drive further efficiencies in their business models. In the case of finance departments, digital transformation is already making a discernible impact, with intelligent software tools and robotic innovations already partially or fully assisting with repetitive tasks. This is reflected in findings from Onguard’s 2020 FinTech Barometer, with 84% of finance professionals agreeing that digital transformation is high on their agenda.

While these innovations may send warning signals to those who are concerned about robots replacing people in their jobs, the reality is actually quite different. The removal of these repetitive actions in fact allows finance professionals to devote their time to value adding tasks that they simply couldn’t make time for previously. Robotic process automation (RPA) therefore has a key role to play in the future. With the Covid-19 pandemic creating greater workloads and uncertainty for finance professionals, any time they can gain back will prove to be crucial.

Where humans can make the difference

Ultimately, a company’s survival depends greatly on its level of returning customers who are satisfied with the service they receive. As businesses look to create a positive customer experience, this is more likely to happen when the order-to-cash process runs as smoothly as possible, and this is made available with the help of automated systems.

Due to the economic impact of Covid-19, an unfortunate consequence has meant more customers have been unable to pay their invoices because of cash flow problems. It is therefore crucial for organisations to put the customer first and ensure that tailor-made solutions are delivered with the individual customer in mind.

While data does have the capability of providing a level of background into a customer’s current situation, it is of course limited by its inability to factor in personal circumstances when looking at non-payment. It is of course crucial that outstanding invoices are paid in a business context, however it is important to identify the reasons for non-payment when considering long-term customer relationships. In these exceptional cases, it’s pivotal that human contact is made to ensure an understanding and empathetic approach towards the customer, and this is exactly the unique offering that a finance professional can bring to the table by taking on this responsibility.

With the finance professional taking this approach in a sensitive situation, the customer receives a positive experience, despite the difficult circumstances they may be in, and has the opportunity to control and resolve their payment problems. This in turn increases the chance of invoices being paid again in future, and the relationship can be maintained.

Freeing up time for the finance professional

By enabling the automation of what used to be the day-to-day tasks of the finance professional, digital transformation is helping to free up time for professionals to apply their skills elsewhere in the business. For example, finance professionals are able to undertake an in-depth review of where any cash flow problems lie, or focus on strategic planning. Also, with the use of specialised order-to-cash and credit management solutions that are powered by artificial intelligence (AI), over time, data acquires predictive value. Finance professionals can take advantage of this to interpret data in a better and faster way, putting them one step ahead.

With greater insights and predictive values, finance professionals can find it easier to make decisions with both the business’ and customers’ interests in mind. The end result is not only increased organisational efficiency, but also higher customer satisfaction and ultimately, retention and repeat business from customers.

People power

While some may have feared that technological innovations would remove finance professionals from their jobs, it has in fact created new opportunities in this sector for employees. They can now devote their time to more value adding tasks, with the repetitive actions taken care of through automation. Not only this, but fewer mistakes are made in the cash-to-order process, thanks to the integration of innovative technologies such as automation and AI. Finance professionals can benefit from insights to help shape their future decisions and ultimately benefit their customers.

Despite the clear advantages of digital transformation in complementing the duties of the finance professional, customisation and a personal approach towards customers are needed, especially in the context of the Covid-19 pandemic. This human touch is something that technology can simply not provide. Through applying a personable approach, the finance professional can continue to make tangible difference in the cash-to-order process, both now and in the years to come.

Marieke Saeij CEO Onguard

Explainable AI (XAI): driving forward financial services

Artificial intelligence (AI) is transforming the world we live in. Constantly evolving and advancing, AI systems are used to optimise investment portfolios, assess insurance claims, trade millions of financial instruments and assign credit scores.

However, to establish the trust needed to deploy these advancements to their full potential we need a framework to increase our understanding of how AI arrives at its findings and suggestions. This will shine a light of clarity on the processes behind AI. Many of today’s advanced machine learning algorithms that power AI systems are inspired by the processes of the human brain, but are constrained by their lack of human ability to explain actions or reasoning.

Because of this gap, an entire research field is now working towards describing the rationale behind AI decision-making. This is known as Explainable AI (XAI). While modern AI systems demonstrate performance and capabilities far beyond previous technologies, practicality and legal compliance can inhibit successful implementation.

XAI will be a key deciding factor for organisations looking to utilise AI effectively due to its ability to help foster innovation, enable compliance with regulations, optimise model performance, and enhance competitive advantage.

What is explainable AI’s value in financial services?

In financial services, the techniques of explainability are becoming especially valuable. When it comes to financial data, many service providers and consultants may already be aware of the low signal-to-noise ratio that is typical of this data, which in turn demands a strong feedback loop between user and machine.

AI solutions that are designed without human feedback capabilities run the risk of never being adopted due to the persistence of traditional approaches that rely on domain expertise and experience from years gone by. AI-powered products that are not auditable will simply struggle to enter the market as they’ll face regulation issues.

A synergy between AI and domain expertise

Financial services have been increasingly embracing time series forecasting methods as useful tools for predicting asset returns, econometric data, market volatility and bid-ask spreads. However, they are limited by their dependence on historical values. As they can lack disparate, meaningful information of the day, it is extremely challenging to use time series to predict the most likely value of a stock or market volatility.

By complementing such models with explainability methods, users can understand the key signals the model uses in its prediction, and interpret the output based on their own complementary view of the market. This then enables a real synergy between finance specialists’ domain expertise and the big data-crunching abilities of modern AI.

Explainability techniques also enable human-in-the-loop AI solutions for portfolio selection. An investor might find that they choose not to pick the suggested portfolio with the highest reward if the level of risk appears too great. On the other hand, a system that provides a detailed explanation of the risks, such as how they could be uncorrelated with the market, is a powerful addition to investment planning tools. Assigning or denying credit to an applicant is a consequential decision that is highly regulated to ensure fairness. The success of AI applications in this field depends on the ability to provide a detailed explanation of final recommendations.

Beyond compliance, the value of XAI is seen for the client and financial institution in different ways. Clients can receive explanations that give them the information they need to improve their credit profile, while service providers can better understand predicted client churn and adapt their services.

Through use of XAI, credit-scoring can also help with reducing risk. For example, an XAI model might provide an explanation of why a pool of assets has the best distribution to minimise the risk of a covered bond.

Powering human-AI collaboration

It is now essential to recognise the importance of prioritising explainability to power human-AI collaboration and to satisfy audit, regulatory and adoption requirements. This is because AI solutions are now evolving beyond proof-of-concept to deployment at scale. AI systems are becoming more capable and finding their way to new industries and applications.

These enhanced capabilities mean more complexity, and that makes these systems more difficult to understand. Taking a user-centric approach along with the imperative for transparency across AI systems together reinforce the need for explainability to be a part of that cycle. All the way from the initial process of building a solution, right to the system integration and use.